Microsoft Technology Licensing, LLC

Tobias Benjamin Schnabel

Susan Theresa Dumais

Paul Nathan Bennett

Longqi Yang

The present concepts relate to a differentiable user-item co-clustering ('DUICC') model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters&#x2014;groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations.

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Redmond, WA, United States of America

Tobias Benjamin Schnabel